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As health care moves from a fee‐for‐service environment to a capitated arena, outcome measurements must change. ABC Children’s Medical Center is challenged with developing comprehensive outcome measures for an employed physician group. An extensive literature review validates that physician outcomes must move beyond revenue production and measure all aspects of care delivery. The proposed measurement model for this physician group is a trilogy model. It includes measures of cost, quality, and service. While these measures can be examined separately, it is imperative to understand their integration in determining an organization’s competitive advantage. The recommended measurements for the physician group must be consistent with the overall organizational goals. The long‐term impact will be better utilization of resources. This will result in the most cost effective quality care for the health care consumer

Researchers in economics and other disciplines are often interested in the causal effect of a binary treatment on outcomes. Econometric methods used to estimate such effects are divided into one of two strands depending on whether they require unconfoundedness (i.e., independence of potential outcomes and treatment assignment conditional on a set of observable covariates). When this assumption holds, researchers now have a wide array of estimation techniques from which to choose. However, very little is known about their performance – both in absolute and relative terms – when measurement error is present. In this study, the performance of several estimators that require unconfoundedness, as well as some that do not, are evaluated in a Monte Carlo study. In all cases, the data-generating process is such that unconfoundedness holds with the ‘real’ data. However, measurement error is then introduced. Specifically, three types of measurement error are considered: (i) errors in treatment assignment, (ii) errors in the outcome, and (iii) errors in the vector of covariates. Recommendations for researchers are provided.

Discusses measuring outcomes in the context of disease management and provides a single framework in the form of a key question checklist. Identifies key stakeholders. Outlines levels of outcome monitoring, measurement and date type and source. The development of an evaluative culture is essential to successful outcome measurements.

Notes limitations to measuring the performance of design activity in particular, and non‐production activities in general. First, validity and reliability in specific measures are strongly negatively correlated, making it hard to achieve both. Second, outcome measures are jointly determined by engineering design and other activities to varying degrees, and this problem of shared outcomes is only partly reduced by measuring at higher levels of aggregation. Third, there is no definite stopping rule for engineering design activity, yet unambiguous outcome measures rely on the existence of such a rule. Fourth, outcomes attributable to engineering design can sometimes only be measured a long time after completion of the activity, making them ineffective for most managerial purposes. There are also considerable problems in properly accounting for environmental variables. However, the use of performance measures have some benefits, e.g. correcting wrong inferences among engineering managers. Results point to the appropriate use of performance measurement in engineering design for raising questions and detecting discrepancies in performance at aggregate levels. They suggest that using measurement is inappropriate for managerial control, for attributing results to engineers or the environment, and for concluding problem solving activities.

Progress monitoring and data-based intervention are unique special education developments stemming from efforts to find an effective alternative to diagnostic/prescriptive instruction. Springing from research on Curriculum-based Measurement (CBM) in the late 1970s and early 1980s at the Minnesota Institute for Research on Learning Disabilities, the approach has generated a large body of empirical research and development. While the original work demonstrated that teachers could be more effective using progress monitoring in data-based intervention, most research and development activity has focused on development and extensions of the CBM model with less attention to data-based intervention. While research on progress monitoring has occurred at a high rate, widespread implementation of progress monitoring has been spurred by both federal funding and commercial development. As might be expected, all of this activity has resulted in a large set of successes and disappointments that are described here. For better or worse, as progress monitoring and data-based intervention have been incorporated into Response to Intervention (RTI) models it seems likely that the future of progress monitoring and data-based intervention is tied to the future of RTI. The question is whether this linking will result in adding to the set of successes or to that of disappointments for this unique special education innovation.

The essential investments in new product development (NPD) made by industrial companies entail effective management of NPD activities. In this context, performance measurement is one of the means that can be employed in the pursuit of effectiveness.

This paper seeks to describe the development process for the Outcomes Stars as a suite of tools which are designed to simultaneously measure and support change when working with vulnerable people as service users. It describes the original process of development of the first Star, in homelessness services in the UK, and subsequent roll out to other client groups and in other countries. The paper indicates the theoretical and philosophical under‐pinning of an approach which aims to embody both research and values‐based practice in empowerment and respect for the individual.

As a case study of development by the development team, the paper is based on first‐hand knowledge but builds upon extensive consultations with practitioners and users and relates these to the needs and strengths of service users, the contemporary policy framework, and wider research in the field.

The Outcomes Star draws on the core principles of Action Research and Participatory Action Research and extends them beyond research into assessment and outcome measurement. As yet there has been no formal research on the usefulness of the Star approach; but, there is a rapid take up of this approach within the UK and further afield. The paper argues that the approach has proved popular, because the Outcomes Star is rooted in a philosophy that is more in tune with that of people delivering services and more closely reflects the reality of those receiving services, compared to traditional measurement techniques.

The paper aims to stimulate further thought and effective practice in measuring outcomes for vulnerable people, and on the most useful means to engage and support people in the co‐production of their own futures.

Although the development and take‐up of the Outcomes Star approach has been rapid in practice, this is the first paper in which these more theoretical and philosophical roots have been outlined and explored in such depth. It will be of use to service providers, to deepen their awareness, and to commissioners, policy makers and regulatory bodies wishing to promote practical approaches to quality assurance of evidence‐based and evidence‐generating practice. It will also be of interest to moral philosophers and others wishing to understand the translation of values into social practice.

The paper explores the literature concerning outcome measures used in health services. The need to measure outcomes subsequent to encounters with health services has been identified and occurs as a result of the current “value for money” approaches being used within the NHS. Provider units are required to establish the effects which interventions have had on the health of each individual using their services, despite the fact that definitions of health outcomes used by both professionals and managers are problematic. It is suggested here, however, that outcome measures which answer all requirements will remain elusive, and their effectiveness will vary according to the circumstances of their generation and use. Moreover, the very use of outcome measures as management tools can lead to a subversion of the meaning which led to their selection in the first place. Managing by (outcome measure) numbers is not a realistic way forward.